U. S. Department of Transportation



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Weather Variables


These variables capture the weather conditions surrounding the incident. As described previously, the weather data originates from the METAR data archived by Plymouth University. It is then interpolated to represent a best approximation of the conditions at the time of the incident.

Temperature


(Weather Database)

The temperature at the time of the incident is interpolated from the closest hourly readings. Figure 30 presents the overall distribution of temperature, the distribution by severity, and the distribution by incident type. The percentiles of the distribution, conditional on severity and incident type, are presented in Table 132 and Table 134.



figure 30 presents the distribution of temperature. the top left chart is a histogram that indicates the overall frequency of temperature at the time of reading, and is moderately skewed to the left. the top right chart indicates temperature by severity category. all categories are similar, with the average temperature between 50 and 100 degrees. the bottom left chart indicates temperature according to incident type, with all three incident types being similar.

Figure – Distribution of Temperature

The overall distribution is unsurprising. This data covers approximately ten years and the 50 States, the District of Columbia, and U.S. Territories; thus, the range seems reasonable. The overall distribution is skewed slightly left, but not dramatically so. The distribution by severity appears fairly similar. Category A and C incursions appear to have slightly higher median temperatures. One might anticipate that ice (and thus cold temperatures) would have a disproportionate effect on severity, but that does not seem to be the case. The distributions by severity also appear quite similar, with V/PD incidents having a slightly lower median temperature. This may be indicative of the involvement of snow removal vehicles in V/PD incidents.51 To further test these apparent differences by severity and incident type, two Kruskal-Wallis tests were performed, the results of which are presented in Table 133 and Table 135.

Table – Percentiles of Temperature by Severity






10th

25th

50th

75th

90th

A

36

58.35

70.78333

81

88

B

38.225

52

64.825

75.8

81.58333

C

38.31667

54.8

67.93333

79

86

D

37

52.75

66

77.4

84.65

Overall

37

53.76667

66.76667

78

85.13333

Table – Kruskal-Wallis Test Results for Temperature by Severity




A

B

C

D

Number of Observations

122

130

3110

4787

Mean Rank

4529.80

3798.59

4187.81

3997.63



Chi2 score: 18.68

Degrees of Freedom: 3

P-value: 0.00

Table – Percentiles of Temperature by Incident Type




10th

25th

50th

75th

90th

OE

37.93333

54

67.51667

78.46667

85.2

PD

40

54.83333

68

78.63333

85.55

V/PD

32.66667

48.96667

64

75.96667

84

Overall

37

53.76667

66.76667

78

85.13333

Table – Kruskal-Wallis Test Results for Temperature by Incident Type




OE

PD

V/PD

Number of Observations

1222

4945

1982

Mean Rank

4121.64

4197.31

3741.08



Chi2 score: 53.78

Degrees of Freedom: 2

P-value: 0.00


Future Research

  • How changes to operations in adverse weather interact with changes in risk due to the weather


The results for severity indicate that, while jointly different, few of the categories can be declared different from each other. Categories C and D are the only two categories that can be declared different. This is partially due to the smaller sample of A and B incidents, leading to less precise estimates of their distributions. There does not seem to be a trend with severity and temperature. It is unclear how temperature alone might impact severity, but temperature may be a proxy for more specific weather phenomena, such as snow and ice. While snow and ice may impact severity, it is possible current operational practices (such as reducing traffic volume) already compensate for the increased risk of a severe incident. Further research, focusing on these particular phenomena (icy runways and snow) may disentangle the operational effects from the weather effects.


Future Research

  • Cause of increase in V/PDs in cold weather


The test by incident type indicates that the three incident types are jointly different and that V/PD incidents are distinct from both OE and PD incidents (OE and PD incidents are not able to be distinguished). This supports the conclusion drawn from the distributional graph, but provides no further indication as to why V/PDs might have a different distribution of temperature. There is a broad range of factors that could influence V/PD incidents to occur at lower temperatures, including: the national geographic distribution of V/PD incidents, the prevalence of snow removal equipment in V/PD incidents, and changes in airport vehicle driver behavior due to cold weather. It is unlikely that temperature causes V/PDs; investigating factors related to cold weather that may cause V/PDs may be helpful in understanding this distribution and its policy implications.


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